Originally posted Jun 18, 2015
Apply information about the individual instruments on your bench
I suppose design and measurement challenges can be a valuable contribution to job security. After all, if a clever and creative person like you has to struggle to hit the targets and balance the tradeoffs, you can’t be replaced with someone less talented—or by a mere set of algorithms.
However, this general promise of increased job security is scant comfort when you’re dealing with a need to improve yield, reduce the cost of test, increase margins, or otherwise engineer your way out of a jam. From time to time, you need a new tactic or insight that will inspire a novel solution to a problem.
This is ground we have walked before, looking for ways to transcend the “designer’s holy triangle” and previous posts have explained how adding information to the measurement process can be a powerful problem solver. One approach is to take advantage of published measurements of typical performance in test equipment to more accurately estimate measurement uncertainty.
In a comment on the post that described that approach, Joe Gorin explained it clearly: “What good is this accuracy if it is not a warranted specification? How can it be used in my measurement uncertainty computations? This accuracy is of great value even when not warranted. Most of us who deal with uncertainty must conform with ISO standards which call for using the statistical methods of the GUM (Guide to the Expression of Uncertainty in Measurement). The GUM, in an appendix, explains that the measurement uncertainty should be the best possible estimate, not a conservative estimate.”
To arrive at the best possible estimate, another—often overlooked—source of information is available to many of us: calibration reports for individual instruments.
The power level accuracy of an individual microwave signal generator is shown in a report generated during periodic calibration. The guaranteed specification is shown as green dashed lines (high and low limits) while blue dots represent specific measurements and the pink brackets indicate the associated uncertainty.
It may not surprise you that the measured performance of this signal generator is much better than the guaranteed specifications. After all, generic specifications must apply to every one of that model and account for environmental conditions and other factors that apply to only a minority of use cases. In this example, instrument-specific information can be added to the process of determining total measurement uncertainty, yielding a substantial improvement.
Keysight calibration services test all warranted specifications for all product configurations. The resulting calibration data is available online in graphic and tabular form at Infoline for Keysight-calibrated instruments, a process that’s much easier than tracking down paper certificates inside your organization. This testing regime and data access is not universal in the industry, so if you’re not using Keysight calibration services you’ll need to check with your vendor.
The optimal use of this additional information will depend on your needs and the specifics of your measurement situation. So far I’ve only described the availability of the data, but I’m looking deeper into the practicalities of using it and will share more in my next post on this topic.
In addition, a discussion and excellent set of references are available in a paper discussing methods for pass/fail conformance that complies with industry standards.
I didn’t learn about calibration in school and my exposure to it in practice has been sporadic. However, I’ve been learning more about it in the past few months and have been impressed with the measures taken in factory and field calibration to ensure accuracy and determine its parameters. You should take advantage of all that effort—and the calibrations you pay for—whenever it will help.